The associations of implant and patient factors with migration of the tibial component differ by sex
Bibliographic record
Abstract
AIMS: Thresholds of acceptable early migration of the components in total knee arthroplasty (TKA) have traditionally ignored the effects of patient and implant factors that may influence migration. The aim of this study was to determine which of these factors are associated with overall longitudinal migration of well-fixed tibial components following TKA. METHODS: Radiostereometric analysis (RSA) data over a two-year period were available for 419 successful primary TKAs (267 cemented and 152 uncemented in 257 female and 162 male patients). Longitudinal analysis of data using marginal models was performed to examine the associations of patient factors (age, sex, BMI, smoking status) and implant factors (cemented or uncemented, the size of the implant) with maximum total point motion (MTPM) migration. Analyses were also performed on subgroups based on sex and fixation. RESULTS: In the overall group, only fixation was significantly associated with migration (p < 0.001). For uncemented tibial components in males, smoking was significantly associated with lower migration (p = 0.030) and BMI approached significance (p = 0.061). For females with uncemented components, smoking (p = 0.081) and age (p = 0.063) approached significance and were both associated with increased migration. The small number of self-reported smokers in this study warrants cautious interpretation and further investigation. For cemented components in females, larger sizes of tibial component were significantly associated with increased migration (p = 0.004). No factors were significant for cemented components in males. CONCLUSION: 2022;104-B(4):444-451.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".